Advertisement

Translational Behavioral Medicine

, Volume 6, Issue 4, pp 577–586 | Cite as

Engaging research participants to inform the ethical conduct of mobile imaging, pervasive sensing, and location tracking research

  • Camille NebekerEmail author
  • Tiffany Lagare
  • Michelle Takemoto
  • Brittany Lewars
  • Katie Crist
  • Cinnamon S. Bloss
  • Jacqueline Kerr
Original Research

Abstract

Researchers utilize mobile imaging, pervasive sensing, social media, and location tracking (MISST) technologies to observe and intervene with participants in their natural environment. The use of MISST methods and tools introduces unique ethical issues due to the type and quantity of data, and produces raising new challenges around informed consent, risk assessment, and data management. Since MISST methods are relatively new in behavioral research, there is little documented evidence to guide institutional review board (IRB) risk assessment and inform appropriate risk management strategies. This study was conducted to contribute the participant perspectives when considering ethical and responsible practices. Participants (n = 82) enrolled in an observational study where they wore several MISST devices for 1 week completed an exit survey. Survey items focused on the following: 1—device comfort, 2—informed consent, 3—privacy protections, and 4—bystander engagement. The informed consent process reflected participant actual experience. Device comfort and privacy were raised as concerns to both the participants and bystanders. While the majority of the participants reported a positive experience, it is important to note that the participants were volunteers who were not mandated to wear tracking devices and that persons who are mandated may not have a similar response. Findings support strategies proposed in the Kelly et al. (2013) ethical framework, which emphasizes procedures to improve informed consent, protect privacy, manage data, and respect bystander rights when using a wearable camera.

Keywords

Mobile health mHealth Research ethics Pervasive sensing Geo-location Location tracking GIS GPS Wearable camera SenseCam IRB Informed consent Privacy Institutional review board 

Notes

Acknowledgments

We would like to thank the iWatch participants for contributing to this study. We also acknowledge Lindsay Dillon, MPH, who contributed to the development of the survey and Elizabeth Booen, MS, who assisted with the data management.

This study was conducted with the participants from the NCI-funded Validating Machine-Learned Classifiers of Sedentary Behavior and Physical Activity study (PI Kerr, Grant # R01CA164993, IRB # 111160). We also acknowledge the Robert Wood Johnson Foundation’s support of the Connected and Open Research Ethics (CORE) initiative (PI Nebeker, #72876, 2015-2017) and the Impact of Privacy Environments for Personal Health Data on Patients (PI Bloss, R01 HG HG008753)

Conflict of interest

The authors declare that they have no conflicts to report.

Informed Consent Statement

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.

References

  1. 1.
    Kumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013; 45(2): 228-236. doi: 10.1016/j.amepre.2013.03.017.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Jankowska M, Schipperjin J, Kerr J. A framework for using GPS data in physical activity and sedentary behavior studies. Exerc Sport Sci Rev. 2015; 43(1): 48-56.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Zenk S, Schulz A, Odoms-Young A, et al. Feasibility of using Global Positioning Systems (GPS) with diverse urban adults. J Phys Act Health. 2012; 9: 290-296.Google Scholar
  4. 4.
    Kerr J, Marshall SJ, Godbole S, et al. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. Am J Prev Med. 2013; 44(3): 290-296. doi: 10.1016/j.amepre.2012.11.004.CrossRefPubMedGoogle Scholar
  5. 5.
    Doherty AR, Kelly P, Kerr J, et al. Use of wearable cameras to assess population physical activity behaviours: an observational study. Lancet. 2012; 380: S35. doi: 10.1016/S0140-6736(13)60391-8.CrossRefGoogle Scholar
  6. 6.
    Doherty AR, Kelly P, Kerr J, et al. Using wearable cameras to categorise type and context of accelerometer-identified episodes of physical activity. Int J Behav Nutr Phys Act. 2013; 10: 22. doi: 10.1186/1479-5868-10-22.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Oliver M, Doherty AR, Kelly P, et al. Utility of passive photography to objectively audit built environment features of active transport journeys: an observational study. Int J Health Geogr. 2013; 12: 20. doi: 10.1186/1476-072X-12-20.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Hodges S, Williams L, Berry E, et al. SenseCam: a retrospective memory aid. In: Ubicomp 2006, LNCS 4206. 2006; 177–93. doi: 10.1007/11853565.
  9. 9.
    Nebeker C, Linares-Orozco R, Crist K. A multi-case study of research using mobile imaging, sensing and tracking technologies to objectively measure behavior. J Res Adm. 2015; 46(1): 118-137.Google Scholar
  10. 10.
    US Department of Health and Human Services. Protection of human subjects. 45 CFR 46. 1964.Google Scholar
  11. 11.
    National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report: ethical principles and guidelines for the protection of human subjects of research. 1978. Available at: http://ohsr.od.nih.gov/guidelines/belmont.html.
  12. 12.
    Kelly P, Marshall S, Badland H, Kerr J. An ethical framework for automated, wearable cameras in health behavior research. Am J Prev Med. 2013; 1–6. Available at: http://www.sciencedirect.com/science/article/pii/S0749379712008628.
  13. 13.
    Anderson EE, Dubois JM. IRB decision-making with imperfect knowledge: a framework for evidence-based research ethics review. J Law, Med Ethics. 2012; 40(4): 951-969. doi: 10.1111/j.1748-720X.2012.00724.x.CrossRefGoogle Scholar
  14. 14.
    Christie G, Patrick K, Schmuland D. Consultation for collective action on personalized health technology: eliminating the ethical, legal and social barriers for individual and societal benefit. J Health Commun. 20:867–868.Google Scholar

Copyright information

© Society of Behavioral Medicine 2016

Authors and Affiliations

  1. 1.Center for Wireless and Population Health SystemsThe Qualcomm Institute, Calit2La JollaUSA
  2. 2.Department of Family Medicine and Public Health, School of MedicineUniversity of CaliforniaSan DiegoUSA
  3. 3.Department of Psychiatry, School of MedicineUniversity of CaliforniaSan DiegoUSA

Personalised recommendations